R Notebook
Samples
CTRL IS WD
1303 1033 1181 # The [[ operator can add columns to object metadata. This is a great place to stash QC stats
oligos[["percent.mt"]] <- PercentageFeatureSet(oligos, pattern = "^mt-")
# Visualize QC metrics as a violin plot
VlnPlot(oligos, group.by = "Sample",features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 1,pt.size = 0.1)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(oligos, group.by = "Sample",feature1 = "nCount_RNA", feature2 = "percent.mt",pt.size = 0.5)
plot2 <- FeatureScatter(oligos, group.by = "Sample", feature1 = "nCount_RNA", feature2 = "nFeature_RNA",pt.size = 0.5)
CombinePlots(plots = list(plot1, plot2))CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
#Clean up the data
oligos <- subset(oligos, subset = nFeature_RNA > 500 & nFeature_RNA < 7000 & percent.mt < 10)
ncol(oligos)[1] 1975# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
oligos[["percent.mt"]] <- PercentageFeatureSet(oligos, pattern = "^mt-")
# Visualize QC metrics as a violin plot
VlnPlot(oligos, group.by = "Sample",features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 1,pt.size = 0.1)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(oligos, group.by = "Sample",feature1 = "nCount_RNA", feature2 = "percent.mt",pt.size = 0.5)
plot2 <- FeatureScatter(oligos, group.by = "Sample", feature1 = "nCount_RNA", feature2 = "nFeature_RNA",pt.size = 0.5)
CombinePlots(plots = list(plot1, plot2))CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
Now we normalize the dataset.
Generating the UMAP and TSNE.
#DefaultAssay(oligos.integrated) <- "integrated"
oligos.integrated <- RunPCA(oligos.integrated, verbose = FALSE)
oligos.integrated <- RunUMAP(oligos.integrated, dims = 1:30)23:07:44 UMAP embedding parameters a = 0.9922 b = 1.112
23:07:44 Read 1975 rows and found 30 numeric columns
23:07:44 Using Annoy for neighbor search, n_neighbors = 30
23:07:44 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:45 Writing NN index file to temp file /var/folders/98/j14xl9ln5190l9fvq32g5rkw0000gn/T//Rtmp9yzIRl/filec2be4cb4393b
23:07:45 Searching Annoy index using 1 thread, search_k = 3000
23:07:45 Annoy recall = 100%
23:07:46 Commencing smooth kNN distance calibration using 1 thread
23:07:46 Initializing from normalized Laplacian + noise
23:07:49 Commencing optimization for 500 epochs, with 77718 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:07:53 Optimization finished#oligos.integrated <- RunTSNE(oligos.integrated, dims = 1:30)
plots <- DimPlot(oligos.integrated, group.by = c("Sample"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
# plots <- TSNEPlot(oligos.integrated, group.by = c("Sample"), combine = FALSE)
# plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
# byrow = TRUE, override.aes = list(size = 3))))
# CombinePlots(plots)Label transfer
Now we attempt to transfer the cluster labels of the Science dataset onto the 10X dataset.
oligos.integrated <- FindNeighbors(oligos.integrated, dims = 1:30)Computing nearest neighbor graph
Computing SNNoligos.integrated <- FindClusters(oligos.integrated,algorithm = 4,resolution = 0.6)
#0.6oligos.integrated$predicted.id <- factor(oligos.integrated$predicted.id,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
DimPlot(oligos.integrated, group.by = c("seurat_clusters"), combine = FALSE)[[1]]
DimPlot(oligos.integrated, group.by = c("predicted.id"), combine = FALSE)[[1]]
DimPlot(oligos.integrated, group.by = c("Sample"), combine = FALSE)[[1]]
table(oligos.integrated$Sample,oligos.integrated$predicted.id)
OPC COP NFOL1 MFOL1 MFOL2 MOL1 MOL2 MOL3 MOL4 MOL5 MOL6 PPR
CTRL 37 5 2 6 2 77 153 8 15 156 264 1
IS 8 1 5 1 1 272 80 6 15 180 62 12
WD 33 5 3 2 0 26 122 5 10 118 269 4data <- as.data.frame(table(oligos.integrated$Sample,droplevels(as.factor(oligos.integrated$predicted.id))))
colnames(data) <- c("Condition","Cluster","Freq")
library(plyr)
data$Cluster <- factor(data$Cluster,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
data <- data[which(data$Cluster %in% c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6")),]
#data$Cluster <- factor(data$Cluster,levels=c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6"))
library(reshape2)
datacasted <- dcast(data,Cluster ~ Condition)Using Freq as value column: use value.var to override.calc_cpm <-function (expr_mat)
{
norm_factor <- colSums(expr_mat)
return(t(t(expr_mat)/norm_factor))
}
datacasted[,2:4] <- calc_cpm(datacasted[,2:4])
data <- melt(datacasted)Using Cluster as id variablescolnames(data) <- c("Condition","Cluster","Freq")
#data$Cluster <- revalue(as.factor(data$Cluster),c("PPR"="VLMC"))
# Stacked + percent
ggplot(data, aes(fill=Condition, y=Freq, x=Cluster)) +
geom_bar(position="fill", stat="identity")
ggplot(data, aes(fill=Cluster, y=Freq, x=Condition)) +
geom_bar( stat="identity")
row.names(datacasted) <- datacasted[,1]
datacasted <- datacasted[,2:4]*100
datamelted <- melt(t(datacasted))
ggplot(datamelted, aes(y = value, x = Var2)) + # Move y and x here so than they can be used in stat_*
geom_dotplot(aes(fill = Var1), # Use fill = Species here not in ggplot()
binaxis = "y", # which axis to bin along
binwidth = 1, # Minimal difference considered diffeerent
stackdir = "center",
position = position_jitter(0.2)# Centered
) + # scale_y_log10() +
stat_summary(fun.y = mean, fun.ymin = mean, fun.ymax = mean,
geom = "crossbar", width = 0.5,fatten = 0.01) + theme(axis.text.x = element_text(angle = 45))
library(heatmap3)
library(viridis)
data <- as.data.frame(table(oligos.integrated$Sample,droplevels(as.factor(oligos.integrated$predicted.id))))
colnames(data) <- c("Condition","Cluster","Freq")
library(plyr)
data$Cluster <- factor(data$Cluster,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
data <- data[which(data$Cluster %in% c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6")),]
#data$Cluster <- factor(data$Cluster,levels=c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6"))
library(reshape2)
datacasted <- dcast(data,Cluster ~ Condition)Using Freq as value column: use value.var to override.calc_cpm <-function (expr_mat)
{
norm_factor <- colSums(expr_mat)
return(t(t(expr_mat)/norm_factor))
}
datacasted[,2:4] <- calc_cpm(datacasted[,2:4])*100
row.names(datacasted) <- datacasted[,1]
datacasted <- datacasted[,2:4]
comparison <-datacasted-apply(datacasted,1,function(x) mean(x))
comparison <-datacasted-datacasted[,1]
heatmap3(comparison[rev(row.names(comparison)),], Rowv = NA , Colv = NA ,scale = "none",symm = F, method = "ward.D2",col=viridis(1000),balanceColor =F,cexRow = 1,cexCol = 1,margins = c(10, 10))
library(RColorBrewer)
heatmap3(comparison[rev(row.names(comparison)),], Rowv = NA , Colv = NA ,scale = "none",symm = F, method = "ward.D2",col=rev(colorRampPalette(brewer.pal(1024,"RdBu"))(1024)),balanceColor =T,cexRow = 1,cexCol = 1,margins = c(10, 10))n too large, allowed maximum for palette RdBu is 11
Returning the palette you asked for with that many colors
relationshipratio <- cor(t(comparison),method="pearson")
heatmap3(relationshipratio[rev(row.names(comparison)),], Rowv = NULL , Colv = NULL ,scale = "none",symm = F, method = "ward.D2",col=colorRampPalette(c("limegreen","black",
"firebrick3"))(1024),balanceColor =F,cexRow = 2,cexCol = 2,margins = c(10, 10))
barplot(table(oligos.integrated$Sample,oligos.integrated$predicted.id))
data <- as.data.frame(table(oligos.integrated$Sample,oligos.integrated$predicted.id))
colnames(data) <- c("Condition","Cluster","Freq")
library(plyr)
data$Cluster <- factor(data$Cluster,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
data$Cluster <- revalue(as.factor(data$Cluster),c("PPR"="VLMC"))
dataIS_CTRL <- data[which(data$Condition %in% c("IS","CTRL")),]
dataWD_CTRL <- data[which(data$Condition %in% c("WD","CTRL")),]
dataIS_WD <- data[which(data$Condition %in% c("IS","WD")),]
# Stacked + percent
ggplot(data, aes(fill=Condition, y=Freq, x=Cluster)) +
geom_bar(position="fill", stat="identity")
ggplot(dataIS_CTRL, aes(fill=Condition, y=Freq, x=Cluster)) +
geom_bar(position="fill", stat="identity")
ggplot(dataWD_CTRL, aes(fill=Condition, y=Freq, x=Cluster)) +
geom_bar(position="fill", stat="identity")
ggplot(dataIS_WD, aes(fill=Condition, y=Freq, x=Cluster)) +
geom_bar(position="fill", stat="identity")
#subset OPCs
oligos.integratedIm <- subset(oligos.integrated,seurat_clusters %in% c(12,11,8,9))
oligos.integratedIm <- FindVariableFeatures(oligos.integratedIm)Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|DefaultAssay(oligos.integratedIm) <- "SCT"#Spatially filter genes with auto bootstrapping #Script will give error when running out of genes, this is normal
networkExpressionFile=oligos.integratedIm@assays$SCT@scale.data
expr_limit <- min(networkExpressionFile)-0.01
featureselection <- row.names(networkExpressionFile)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3449124 184.3 5582874 298.2 5582874 298.2
Vcells 355412298 2711.6 659849889 5034.3 878394132 6701.7library(umap)
useUMAP <- "TRUE"
UMAPdim <- 3
adjustforbias <- "FALSE"
GeneMarkovList <- list()
GeneFilterProgression <- as.data.frame(featureselection)
row.names(GeneFilterProgression) <- featureselection
corenumber <- 4
usemagic <- "FALSE"
pcathresh <- 20
ncompGF <- 20
whentobootstrap <- 20000 #cellnumber
howmanyboots <- 10+1 #bootstrap repetitions
samplesize <- 2000 #how many cells to take for sampling
threshold <- 3 #use mean spatial correlation of geneset of the featureselection (1) or after the first round of filtering (2) (more strict, which is default)
nsim <- 100
nnquant <- 0.995 #lower for small datasets, higher for speedups in larger datasets
tri.to.squ<-function(x)
{
rn<-row.names(x)
cn<-colnames(x)
an<-unique(c(cn,rn))
myval<-x[!is.na(x)]
mymat<-matrix(1,nrow=length(an),ncol=length(an),dimnames=list(an,an))
for(ext in 1:length(cn))
{
for(int in 1:length(rn))
{
if(is.na(x[row.names(x)==rn[int],colnames(x)==cn[ext]])) next
mymat[row.names(mymat)==rn[int],colnames(mymat)==cn[ext]]<-x[row.names(x)==rn[int],colnames(x)==cn[ext]]
mymat[row.names(mymat)==cn[ext],colnames(mymat)==rn[int]]<-x[row.names(x)==rn[int],colnames(x)==cn[ext]]
}
}
return(mymat)
}
SCN3Egeneset <- featureselection
originalnetworkdf <- networkExpressionFile
if(ncol(networkExpressionFile)>whentobootstrap){
originalnetworkdf <- networkExpressionFile
bootstrap<-1
sample<-1
moransIsampled <- as.character(NULL)
}
if(ncol(networkExpressionFile)<=whentobootstrap)
{
bootstrap<-howmanyboots-1
sample<-0
}
while(bootstrap<howmanyboots){
SCN3Egeneset <- featureselection
iter=0
meanMoran<-0
if(sample==1){
networkExpressionFile <- originalnetworkdf[,sample(ncol(originalnetworkdf), samplesize) ]
print(paste("bootstrapping...","round",bootstrap))
}
OldGeneset <- as.character(seq_len(100000))
while(length(OldGeneset) > (length(SCN3Egeneset)+10) ){ # uncomment this and below to enable iterative filtering
iter=iter+1
#library(amap)
#library(MASS)
#library(destiny)
#library(dpt)
#library(Matrix)
#library(diffusionMap)
print(paste("Preparing genefiltering on", length(SCN3Egeneset), "genes."))
#print("Making celllandscape...Filtering possible duplicated cells in original file")
#if(length(OldGeneset)==100000)
#{
# pca <- prcomp(t(log(networkExpressionFile[featureselection,]+1)),scale. = FALSE)
# cd_diffusionplot <-pca$x[,c(1:30)]
# }
#if(length(OldGeneset)!=100000){cd_diffusionplot <- t(log(networkExpressionFile[SCN3Egeneset,]+1))}
if(length(SCN3Egeneset) > pcathresh){
#library(rsvd)
print("Making celllandscape...Performing dimensionality reduction")
#pca <- rpca(t(log(networkExpressionFile[SCN3Egeneset,]+1)))
#pca <- prcomp(t(log(networkExpressionFile[SCN3Egeneset,]+1)),scale. = TRUE)
pca <- prcomp(t(networkExpressionFile[SCN3Egeneset,]),scale. = TRUE)
if(length(SCN3Egeneset) < 100) {ncompGF <- length(SCN3Egeneset)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.8)[1])]
}
if(length(SCN3Egeneset) <= pcathresh){
#cd_diffusionplot <-t(log(networkExpressionFile[SCN3Egeneset,]+1))
cd_diffusionplot <-t(networkExpressionFile[SCN3Egeneset,])
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
print("Making celllandscape...Making diffusionmap")
# gc()
#ts <- Transitions(cd_diffusionplot,k=20)
if(useUMAP==TRUE)
{
library(umap)
umap.settings <- umap.defaults
umap.settings$n_neighbors <-10
umap.settings$min_dist <- 0
umap.settings$n_components <- UMAPdim
umap.settings$metric <- "manhattan"
#umap.settings$local_connectivity <- 0.00001
umap.settings$n_epochs <- 1000
umap_out <- umap(cd_diffusionplot,config = umap.settings,method="umap-learn")
}
if(useUMAP != TRUE){
library(destiny)
ts <- DiffusionMap(cd_diffusionplot, verbose = TRUE)
}
if(adjustforbias==TRUE){
testwilcox <- as.matrix(apply(t(ts@eigenvectors),1,function(x) apply(tri.to.squ(pairwise.wilcox.test(x,biasedfactor,p.adjust.method = "fdr")$p.value),2,function(x) min(x,na.rm=TRUE))))
testwilcoxdifference <- scale(t(testwilcox))
biasedcomponents <- unique(unlist(apply(testwilcoxdifference,2,function(x) which(abs(x) > 1.96))))
}
#Gene filtering
print(paste("Filtering", length(SCN3Egeneset), "genes."))
OldGeneset <- SCN3Egeneset
#NetworkDist <- as.matrix(ts@transitions)
library(amap)
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
if(useUMAP==TRUE)
{
if(adjustforbias==TRUE){
NetworkDist <- 1-range01((as.matrix(Dist(umap_out$layout[,1:UMAPdim],method = "manhattan",nbproc = 8))))}
else{NetworkDist <- 1-range01((as.matrix(Dist(umap_out$layout[,1:UMAPdim],method = "manhattan",nbproc = 8))))}
}
if(useUMAP != TRUE){
if(adjustforbias==TRUE){
NetworkDist <- 1-range01((as.matrix(Dist(ts@eigenvectors[,-c(biasedcomponents)],method = "manhattan",nbproc = 8))))}
else{NetworkDist <- 1-range01((as.matrix(Dist(ts@eigenvectors,method = "manhattan",nbproc = 8))))}
}
#NetworkDist <- distcells*distcells2
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
emat_expressed <- apply(networkExpressionFile,1,function(x) any ((x) >expr_limit))
emat_expressed <- networkExpressionFile[emat_expressed,row.names(NetworkDist)]
emat_expressed <- as.matrix(emat_expressed[intersect(row.names(emat_expressed),OldGeneset),])
if(usemagic=="TRUE"){
emat_expressed <- magic(ts,emat_expressed[,row.names(cd_diffusionplot)], power = 1, k = 20, n_eigs = 20, n_local = 10)
}
#library(doParallel)
#library(foreach)
library(parallel)
library(spdep)
cores <- corenumber
# cl <- makeCluster(cores)
# registerDoParallel(cl)
SCN3Egenefilter <- matrix(nrow=nrow(emat_expressed),ncol = 3)
cellnames <- colnames(emat_expressed)
# library(Matrix)
# NetworkDistsparse <- Matrix(NetworkDist, sparse = TRUE)
print("Making celllandscape...Generating Spatial Weights Matrix...")
i=1
NetworkDist2 <- matrix(nrow=nrow(NetworkDist),ncol=ncol(NetworkDist))
for(i in 1:ncol(NetworkDist2))
{
x <-NetworkDist[,i]
x[x < as.numeric(quantile(x,nnquant))] <- 0
NetworkDist2[,i] <- x
}
NetworkDist <- NetworkDist2
rm(NetworkDist2)
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
spatial.weights <- mat2listw(NetworkDist[])
print("Making celllandscape...Generating Spatial Weights Matrix...done")
numbsim <- nsim
i=1
test <- as.data.frame(mclapply(1:nrow(emat_expressed), function(i) {
return(m <- c(unlist(moran.mc(emat_expressed[i,],spatial.weights,nsim=numbsim,zero.policy = TRUE))[1:3],row.names(emat_expressed)[i]))
}, mc.cores=corenumber))
SCN3Egenefilter <- t(test)
SCN3Egenefilter <- SCN3Egenefilter[! apply(SCN3Egenefilter,1,function(x) any(x=="NaN")),]
row.names(SCN3Egenefilter) <- SCN3Egenefilter[,4]
SCN3Egenefilter_clean <- as.data.frame(SCN3Egenefilter[complete.cases(SCN3Egenefilter),c(1:4)])
r <- row.names(SCN3Egenefilter_clean)
SCN3Egenefilter_clean <- apply(SCN3Egenefilter_clean,2,function(x) as.numeric(x))
row.names(SCN3Egenefilter_clean) <- r
SCN3Egeneset <- row.names(subset(SCN3Egenefilter_clean,SCN3Egenefilter_clean[,3] <= 0.01))
HSEgenes <- row.names(subset(SCN3Egenefilter_clean,SCN3Egenefilter_clean[,3] <= 0.01 & SCN3Egenefilter_clean[,1] >= as.numeric(quantile(SCN3Egenefilter_clean[SCN3Egeneset,1],0.1))))
SCN3Egeneset <- row.names(subset(SCN3Egenefilter_clean,SCN3Egenefilter_clean[,3] <= 0.01 & SCN3Egenefilter_clean[,1] >= as.numeric(quantile(SCN3Egenefilter_clean[SCN3Egeneset,1],0.1))))
GeneFilterProgression <- cbind(GeneFilterProgression[SCN3Egeneset,],SCN3Egenefilter_clean[SCN3Egeneset,1])
if(iter<=threshold){meanMoran<-mean(abs(SCN3Egenefilter_clean[,1]),na.rm=TRUE)}
GeneMarkovList <- c(list(GeneMarkov=SCN3Egeneset),GeneMarkovList)
} #uncomment this to enable iterative filtering
bootstrap <- bootstrap+1
if(sample==1){
moransIsampled <- c(moransIsampled,SCN3Egeneset)
}
}[1] "Preparing genefiltering on 3000 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 3000 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 1848 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 1848 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 1571 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 1571 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 1383 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 1383 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 1227 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 1227 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 1100 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 1100 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 987 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 987 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 886 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 886 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 796 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 796 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 715 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 715 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 643 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 643 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 578 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 578 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 520 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 520 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 468 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 468 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 421 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 421 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 379 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 379 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 341 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 341 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 307 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 307 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 276 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 276 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 247 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 247 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 222 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 222 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 199 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 199 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 179 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 179 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 161 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 161 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 145 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 145 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 130 genes."
[1] "Making celllandscape...Performing dimensionality reduction"
[1] "Making celllandscape...Making diffusionmap"
[1] "Filtering 130 genes."
[1] "Making celllandscape...Generating Spatial Weights Matrix..."
[1] "Making celllandscape...Generating Spatial Weights Matrix...done"NAs introduced by coercion[1] "Preparing genefiltering on 0 genes."
[1] "Making celllandscape...Making diffusionmap"Error in d[, 1] : subscript out of boundslength_iterations <- unlist(lapply(GeneMarkovList,function(x) length(x)))
plot(seq_along(length_iterations),length_iterations)
pcatsne <- prcomp(t(as.matrix(networkExpressionFile[unlist(GeneMarkovList[26]),])),scale. = TRUE)
pca <- pcatsne
plot(log(pcatsne$sdev[1:50]),pch = 20,
xlab = 'Principal component', ylab = 'sdev')
ncompL1tsne <- which(cumsum(pca$sdev[1:50])/sum(pca$sdev[1:50]) > 0.6)[1]
tsne <- t(pca$x[,c(1:ncompL1tsne)])
number_of_clusters <- 8
NetworkDist <- Dist(t(tsne),method = "manhattan",nbproc = 8)
GeneMarkov_hc <- hclust(NetworkDist, method = "ward.D2")
GMcluster <- rbind(groups = cutree(GeneMarkov_hc, k=number_of_clusters))
names(GMcluster) <- colnames(networkExpressionFile)
oligos.integratedIm$seurat_clusters <- as.factor(GMcluster)
GeneMarkov <- unlist(GeneMarkovList[27])DefaultAssay(oligos.integratedIm) <- "SCT"
oligos.integratedIm <- RunPCA(oligos.integratedIm, verbose = FALSE,features = GeneMarkov)#npcs = 20)
ElbowPlot(oligos.integratedIm)
oligos.integratedIm <- RunUMAP(oligos.integratedIm, dims = 1:10)23:16:02 UMAP embedding parameters a = 0.9922 b = 1.112
23:16:02 Read 203 rows and found 10 numeric columns
23:16:02 Using Annoy for neighbor search, n_neighbors = 30
23:16:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:16:02 Writing NN index file to temp file /var/folders/98/j14xl9ln5190l9fvq32g5rkw0000gn/T//Rtmp9yzIRl/filec2be1c0a2654
23:16:02 Searching Annoy index using 1 thread, search_k = 3000
23:16:03 Annoy recall = 100%
23:16:03 Commencing smooth kNN distance calibration using 1 thread
23:16:04 Initializing from normalized Laplacian + noise
23:16:04 Commencing optimization for 500 epochs, with 6232 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:16:04 Optimization finished#oligos.integratedScience <- RunTSNE(oligos.integratedScience, dims = 1:10)
plots <- DimPlot(oligos.integratedIm, group.by = c("seurat_clusters"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
plots <- DimPlot(oligos.integratedIm, group.by = c("Sample"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
oligos.integrated.markersIm %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)library(viridis)
DefaultAssay(oligos.integratedIm) <- "SCT"
top10 <- oligos.integrated.markersIm %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
# DoHeatmap(oligos.integratedIm, features = intersect(oligos.integrated.markersIm$gene,GeneMarkov),disp.max=7,) + NoLegend() +scale_fill_viridis()
DoHeatmap(oligos.integratedIm, features =intersect(oligos.integrated.markersIm$gene,unique(c(unlist(GeneMarkovList[5]),top10$gene))),disp.max=3) + NoLegend() +scale_fill_viridis()The following features were omitted as they were not found in the scale.data slot for the SCT assay: Cnp, Aplp1, Mog, Mobp, ErmnScale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing
scale.
#for MT=10%
oligos.integratedOL <- subset(oligos.integrated,seurat_clusters %in% c(1:6,8,9))
#non-integrated
#oligos.integratedOL <- subset(oligos.integrated,seurat_clusters %in% c(1:6,10))
#for MT=5%
#oligos.integratedOL <- subset(oligos.integrated,seurat_clusters %in% c(1:5))
DefaultAssay(oligos.integrated) <- "SCT"
# oligos.integratedOL <- RunPCA(oligos.integratedOL, verbose = FALSE,features=c("Opalin","Ptgds","Apoe","S100b","Apod","Lamp1","Fos","Sepp1"),npcs=5,approx=FALSE)
oligos.integratedOL <- RunPCA(oligos.integratedOL, verbose = FALSE)#,features=GeneMarkov)
ElbowPlot(oligos.integratedOL)
oligos.integratedOL <- RunUMAP(oligos.integratedOL, dims = 1:30)23:17:57 UMAP embedding parameters a = 0.9922 b = 1.112
23:17:57 Read 1758 rows and found 30 numeric columns
23:17:57 Using Annoy for neighbor search, n_neighbors = 30
23:17:57 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:17:57 Writing NN index file to temp file /var/folders/98/j14xl9ln5190l9fvq32g5rkw0000gn/T//Rtmp9yzIRl/filec2be53b3785f
23:17:57 Searching Annoy index using 1 thread, search_k = 3000
23:17:58 Annoy recall = 100%
23:17:58 Commencing smooth kNN distance calibration using 1 thread
23:17:59 Initializing from normalized Laplacian + noise
23:18:00 Commencing optimization for 500 epochs, with 69202 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:18:03 Optimization finished#oligos.integratedScience <- RunTSNE(oligos.integratedScience, dims = 1:10)
plots <- DimPlot(oligos.integratedOL, group.by = c("seurat_clusters"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
plots <- DimPlot(oligos.integratedOL, group.by = c("Sample"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3,
byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
DimPlot(oligos.integratedOL, group.by = c("Sample"), combine = FALSE)[[1]]
DimPlot(oligos.integratedOL, group.by = c("seurat_clusters"), combine = FALSE)[[1]]
DimPlot(oligos.integratedOL, group.by = c("predicted.id"), combine = FALSE,label=TRUE)[[1]]
oligos.integrated.markersOL %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)DefaultAssay(oligos.integratedOL) <- "SCT"
# Normalize RNA data for visualization purposes
#oligos.integrated <- NormalizeData(oligos.integrated, verbose = FALSE)
FeaturePlot(oligos.integratedOL, c("Pdgfra", "Ptprz1","Bmp4","Itpr2", "Egr1","Egr2", "Fos","Klk6", "Hopx", "Ptgds","Il33","Mbp","Cd74","Serpina3n"),pt.size = 1)
FeaturePlot(oligos.integratedOL, c("Opalin","Ptgds","Apoe","S100b","Apod","Lamp1","Fos","Sepp1","Klk6","Hopx"),pt.size = 1)The following requested variables were not found: Sepp1
DefaultAssay(oligos.integrated) <- "SCT"library(ggrepel)
DefaultAssay(oligos.integratedOL) <- "SCT"
Idents(oligos.integratedOL) <- "Sample"
oligos.integrated.samplediffIS <- FindMarkers(oligos.integratedOL, ident.1 = "IS", ident.2 = "CTRL", verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffIS
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.7), label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.7)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 
oligos.integrated.samplediffWD <- FindMarkers(oligos.integratedOL, ident.1 = "WD", ident.2 = c("CTRL"), verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffWD
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.35), label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.35)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 
oligos.integrated.samplediffISWD <- FindMarkers(oligos.integratedOL, ident.1 = "IS", ident.2 = "WD", verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffISWD
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85), label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 
oligos.integrated.samplediffISWDvsCNTRL <- FindMarkers(oligos.integratedOL, ident.1 = c("IS","WD"), ident.2 = "CTRL", verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffISWDvsCNTRL
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85), label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 
DefaultAssay(oligos.integrated) <- "SCT"DiffMatrix <- list()
diffmatrixnames <- c("oligos.integrated.samplediffISWD",
"oligos.integrated.samplediffISWDvsCNTRL")
do.call(head,as.list(as.name(diffmatrixnames[1])))DiffMatrix <- list()
diffmatrixnames <- c("oligos.integrated.samplediffISWD",
"oligos.integrated.samplediffISWDvsCNTRL")
do.call(head,as.list(as.name(diffmatrixnames[1])))library(clusterProfiler)
clusterProfiler v3.10.1 For help: https://guangchuangyu.github.io/software/clusterProfiler
If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.#Convert to gencode using biomart
library(biomaRt)
listMarts()ensembl = useMart("ensembl",dataset="mmusculus_gene_ensembl")
listDatasets(ensembl)attributes = listAttributes(ensembl)
Biomart_gencode_ensembl84_biotypes <- getBM(attributes=c("mgi_symbol","ensembl_gene_id","entrezgene_id","gene_biotype"), filters = "", values = "", ensembl)
Biomart_gencode_ensembl84_biotypes[, 'gene_biotype'] <- as.factor(Biomart_gencode_ensembl84_biotypes[,'gene_biotype'])
#Filter for only our genes
Biotype_All_dataset <- subset(Biomart_gencode_ensembl84_biotypes, mgi_symbol %in% oligos.integrated@assays$SCT@var.features)
entrezID <- subset(Biotype_All_dataset, Biotype_All_dataset$mgi_symbol %in% oligos.integrated@assays$SCT@var.features)# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("reactome.db")
library(ReactomePA)ReactomePA v1.26.0 For help: https://guangchuangyu.github.io/ReactomePA
If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479library(org.Mm.eg.db)Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:dplyr':
combine, intersect, setdiff, union
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap,
parApply, parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:snow':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap,
parApply, parCapply, parLapply, parRapply, parSapply
The following objects are masked from 'package:Matrix':
colMeans, colSums, rowMeans, rowSums, which
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append, as.data.frame, basename, cbind,
colMeans, colSums, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply, setdiff, sort, table,
tapply, union, unique, unsplit, which, which.max, which.min
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite
Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following objects are masked from 'package:dplyr':
first, rename
The following object is masked from 'package:plyr':
rename
The following object is masked from 'package:plotly':
rename
The following object is masked from 'package:Matrix':
expand
The following object is masked from 'package:base':
expand.grid
Attaching package: 'IRanges'
The following objects are masked from 'package:dplyr':
collapse, desc, slice
The following object is masked from 'package:sp':
%over%
The following object is masked from 'package:plyr':
desc
The following object is masked from 'package:plotly':
slice
Attaching package: 'AnnotationDbi'
The following object is masked from 'package:dplyr':
select
The following object is masked from 'package:plotly':
selectReactomeTerms <- list()
i=1
#UP
pvaladj <- 0.01
logfc <- 0.25
for(i in 1:length(diffmatrixnames)){
diffmatrix <- do.call("as.data.frame",as.list(as.name(diffmatrixnames[i])))
diffmatrix <- subset(diffmatrix, p_val_adj < pvaladj & avg_logFC > logfc)
siggenes <- head(row.names(diffmatrix),50)
entrezmatched <- entrezID[entrezID$mgi_symbol %in% siggenes,]
#entrezID <- entrezID[! apply(entrezID[,c(1,3)], 1,function (x) anyNA(x)),]
allLLIDs <- entrezmatched$entrezgene
modulesReactome <- enrichPathway(gene=allLLIDs,organism="mouse",pvalueCutoff=0.01,qvalueCutoff = 0.3,pAdjustMethod = "none", readable=T)
ReactomeTerms[[i]] <- modulesReactome
head(as.data.frame(modulesReactome))
print(i)
}[1] 1
[1] 2ReactomeTerms[which(lapply(ReactomeTerms,function(x) is.null(x))==TRUE)] <- "No_Genes"
#Add DOWN
pvaladj <- 0.01
logfc <- -0.25
offset <- length(ReactomeTerms)
for(i in 1:length(diffmatrixnames)){
i=i+offset
diffmatrix <- do.call("as.data.frame",as.list(as.name(diffmatrixnames[i-offset])))
diffmatrix <- subset(diffmatrix, p_val_adj < pvaladj & avg_logFC < logfc)
siggenes <- head(row.names(diffmatrix),50)
entrezmatched <- entrezID[entrezID$mgi_symbol %in% siggenes,]
#entrezID <- entrezID[! apply(entrezID[,c(1,3)], 1,function (x) anyNA(x)),]
allLLIDs <- entrezmatched$entrezgene
modulesReactome <- enrichPathway(gene=allLLIDs,organism="mouse",pvalueCutoff=0.01,qvalueCutoff = 0.3,pAdjustMethod = "none", readable=T)
ReactomeTerms[[i]] <- modulesReactome
head(as.data.frame(modulesReactome))
print(i)
}[1] 3
[1] 4ReactomeTerms[which(lapply(ReactomeTerms,function(x) is.null(x))==TRUE)] <- "No_Genes"Upper_diff <- subset(oligos.integrated.samplediffISWD, p_val_adj < 0.01 & abs(avg_logFC) > 0)
Lower_diff <- subset(oligos.integrated.samplediffISWDvsCNTRL, p_val_adj < 0.01 & abs(avg_logFC) > 0)
AlldiffgenesHetMOL5 <- intersect(intersect(row.names(oligos.integrated.samplediffISWD),row.names(oligos.integrated.samplediffISWDvsCNTRL)),unique(c(row.names(Upper_diff),row.names(Lower_diff))))
subset2 <- oligos.integrated.samplediffISWD[AlldiffgenesHetMOL5,]
subset3 <- oligos.integrated.samplediffISWDvsCNTRL[AlldiffgenesHetMOL5,]
subsetMOL5 <- cbind(subset2,subset3)
colnames(subsetMOL5) <- make.unique(colnames(subsetMOL5))
diffmatrix <- subsetMOL5
diffmatrix$log_p_val <- -log10(diffmatrix$p_val_adj)
q95pgenes1 <- row.names(diffmatrix[which(diffmatrix$log_p_val >= quantile(diffmatrix$log_p_val,0)),])
diffmatrix$log_p_val.1 <- -log10(diffmatrix$p_val_adj.1)
q95pgenes2 <- row.names(diffmatrix[which(diffmatrix$log_p_val.1 >= quantile(diffmatrix$log_p_val.1,0)),])
q95pgenes <- unique(c(q95pgenes1,q95pgenes2))
diffmatrix <- diffmatrix[q95pgenes,]
diffmatrix$avg_logFC[is.infinite(diffmatrix$avg_logFC)] <- max(diffmatrix$avg_logFC[!is.infinite(diffmatrix$avg_logFC)])
diffmatrix$avg_logFC.1[is.infinite(diffmatrix$avg_logFC.1)] <- max(diffmatrix$avg_logFC.1[!is.infinite(diffmatrix$avg_logFC.1)])
#diffmatrix$avg_logFC.1 <- 2*diffmatrix$avg_logFC.1
diffmatrix$combp <- -log10(diffmatrix$p_val_adj*diffmatrix$p_val_adj.1)
diffmatrix$maxp <- apply(cbind(diffmatrix$log_p_val,diffmatrix$log_p_val.1),1,function(x) max(x))
diffmatrix$minp <- apply(cbind(diffmatrix$p_val_adj,diffmatrix$p_val_adj.1),1,function(x) min(x))
diffmatrix$maxp[is.infinite(diffmatrix$maxp)] <- max(diffmatrix$maxp[!is.infinite(diffmatrix$maxp)])
diffmatrix$maxFC <- apply(cbind(diffmatrix$avg_logFC,diffmatrix$avg_logFC.1),1,function(x) max(abs(x)))
diffmatrix$Genes <- factor(row.names(diffmatrix),levels=row.names(diffmatrix))
ggplot(diffmatrix,aes(avg_logFC,y=avg_logFC.1,colour=maxp,label=row.names(diffmatrix)))+ geom_point(size=diffmatrix$maxp/100) + scale_colour_viridis_c(direction = +1,option ="viridis" ) + geom_hline(yintercept= 0,linetype="dashed",size=0.1,color="cyan")+
geom_hline(yintercept= 0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
geom_hline(yintercept= -0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
geom_vline(xintercept= 0,linetype="dashed",size=0.1,color="cyan")+
geom_vline(xintercept= 0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
geom_vline(xintercept= -0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
geom_text_repel(size=3,fontface = "bold",force=1,data=subset(diffmatrix,
maxp > quantile(diffmatrix$maxp,0.98) #|
# avg_logFC > 0 |
# avg_logFC < -0|
# avg_logFC.1 > 0 |
# avg_logFC.1 < -0)
),label=row.names(subset(diffmatrix,
maxp > quantile(diffmatrix$maxp,0.98) #|
# avg_logFC > 0 |
# avg_logFC < -0 |
# avg_logFC.1 > 0 |
)# avg_logFC.1 < -0)
))+xlab("IS vs WD") + ylab("Other vs Control") +theme(
# get rid of panel grids
panel.grid.major = element_blank(),
#panel.grid.major = element_line(color="darkgrey",size=0.1),
panel.grid.minor = element_blank(),
#panel.grid.minor = element_line(color="darkgrey",size=0.05),
# Change plot and panel background
plot.background=element_rect(fill = "white"),
panel.background = element_rect(fill = 'black'),
# Change legend
legend.background = element_rect(fill = "white", color = NA),
legend.key = element_rect(color = "gray", fill = "white"),
legend.title = element_text(color = "Black"),
legend.text = element_text(color = "black")
)
#magma,inferno, plasma,viridis
#scale_colour_gradient(low = "darkgreen", high = "red")
#Do reactome analysis at the bottom of script
i=1
j=1
#for(i in 1:length(ReactomeTerms)){
for(i in 1:4){
pwydata <- as.data.frame(ReactomeTerms[[i]])
geneset <- strsplit(pwydata$geneID, "/")
FCmeans <- data.frame()
for(j in 1:length(geneset)){
geneset2FC <- which(row.names(diffmatrix) %in% geneset[[j]])
FC <- mean(diffmatrix$avg_logFC[geneset2FC],na.rm=T)
FCvar <- var(diffmatrix$avg_logFC[geneset2FC],na.rm=T)
FC.1 <- mean(diffmatrix$avg_logFC.1[geneset2FC],na.rm=T)
FC.1var <- var(diffmatrix$avg_logFC.1[geneset2FC],na.rm=T)
FCmeans <- rbind(FCmeans,cbind(FC,FC.1,FCvar,FC.1var))
print(j)
}
ReactomeTerms[[i]] <- cbind(ReactomeTerms[[i]],FCmeans)
print(i)
}[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 12
[1] 13
[1] 14
[1] 15
[1] 1
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 12
[1] 13
[1] 14
[1] 15
[1] 16
[1] 17
[1] 18
[1] 19
[1] 20
[1] 21
[1] 22
[1] 23
[1] 24
[1] 25
[1] 26
[1] 27
[1] 28
[1] 29
[1] 30
[1] 31
[1] 32
[1] 33
[1] 34
[1] 35
[1] 36
[1] 2
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 3
[1] 1
[1] 2
[1] 3
[1] 4pathmatrix <- rbind(as.data.frame(ReactomeTerms[[1]]),as.data.frame(ReactomeTerms[[2]]),as.data.frame(ReactomeTerms[[3]]),as.data.frame(ReactomeTerms[[4]]))
#pathmatrix <- rbind(as.data.frame(ReactomeTerms[[1]]),as.data.frame(ReactomeTerms[[2]]))
#pathmatrix <- rbind(as.data.frame(ReactomeTerms[[3]]),as.data.frame(ReactomeTerms[[4]]))
pathmatrix$p.adjust_original <- pathmatrix$p.adjust
pathmatrix$p.adjust <- -log10(pathmatrix$p.adjust )
pathmatrix$maxFC <- sum(abs(pathmatrix$FC),abs(pathmatrix$FC.1))
pathmatrix <- subset(pathmatrix, pathmatrix$Count > 1)
pathmatrix$AdjSelect <- pathmatrix$p.adjust*(500*(0.2+abs(pathmatrix$FC)))
#scale_colour_gradient(low = "yellow", high = "red") +
#scale_colour_viridis_c(direction = -1)
#scale_colour_gradient(low = "black", high = "red")
ggplot(pathmatrix,aes(FC,y=FC.1,colour=p.adjust_original),label=pathmatrix$Description)+ geom_point(size=pathmatrix$Count,alpha=0.5) +scale_colour_viridis_c(direction = +1,option = "viridis") +
geom_hline(yintercept= 0,linetype="solid",size=0.5,color="black",alpha=0.5)+
geom_hline(yintercept= 0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
geom_hline(yintercept= -0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
geom_vline(xintercept= 0,linetype="solid",size=0.5,color="black",alpha=0.5)+
geom_vline(xintercept= 0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
geom_vline(xintercept= -0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
geom_text_repel(size=2,fontface="bold",force=20,data=
subset(pathmatrix,
abs(pathmatrix$AdjSelect) > quantile(
abs(pathmatrix$AdjSelect),1,na.rm=T) | abs(pathmatrix$p.adjust) > quantile(
abs(pathmatrix$p.adjust),0.75,na.rm=T) |
abs(pathmatrix$FC.1) > quantile(abs(pathmatrix$FC.1),1,na.rm=T)),
label=subset(pathmatrix,
abs(pathmatrix$AdjSelect) > quantile(abs(pathmatrix$AdjSelect),1,na.rm=T) |
abs(pathmatrix$p.adjust) > quantile(abs(pathmatrix$p.adjust),0.75,na.rm=T) |
abs(pathmatrix$FC.1) > quantile(abs(pathmatrix$FC.1),1,na.rm=T))$Description,box.padding = 0.5)+xlab("IS vs WD") + ylab("Other vs Control") 
pathmatrixsort <- pathmatrix[order(pathmatrix$FC,decreasing=T),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description))
ggplot(pathmatrixsort, aes(x=FC, y=Description)) +
geom_point(aes(color = p.adjust_original))
pathmatrixsort <- pathmatrix[order(pathmatrix$FC.1,decreasing=T),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description))
ggplot(pathmatrixsort, aes(x=FC.1, y=Description)) +
geom_point(aes(color = p.adjust_original))
pathmatrixsort <- pathmatrix[order(pathmatrix$FC,decreasing=F),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description))
pathmatrixsort <- pathmatrixsort[!pathmatrixsort$p.adjust_original > 0.01,]
library(reshape2)
pathmatrixsortISWD <- pathmatrixsort[,c(2,10,14)]
pathmatrixsortISWD$Group <- rep("ISvsWD",nrow(pathmatrixsortISWD))
pathmatrixsortCntrlISWD <- pathmatrixsort[,c(2,11,14)]
colnames(pathmatrixsortCntrlISWD)[2] <- "FC"
pathmatrixsortCntrlISWD$Group <- rep("ControlvsIS-WD",nrow(pathmatrixsortCntrlISWD))
pathmatrixsort <- rbind(pathmatrixsortISWD,pathmatrixsortCntrlISWD)
ggplot(pathmatrixsort, aes(x=FC, y=Description)) +
geom_line(aes(group = Description)) +
geom_point(aes(color = Group))
pathmatrixsort <- pathmatrix[order(pathmatrix$FC,decreasing=F),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description))
pathmatrixsort <- pathmatrixsort[!pathmatrixsort$p.adjust_original > 0.01,]
library(reshape2)
ggplot(pathmatrixsort, aes(x=FC, y=Description)) +
geom_point(aes(color = Count))
pathmatrixsort <- pathmatrix[order(pathmatrix$FC.1,decreasing=F),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description))
pathmatrixsort <- pathmatrixsort[!pathmatrixsort$p.adjust_original > 0.01,]
library(reshape2)
ggplot(pathmatrixsort, aes(x=FC.1, y=Description)) +
geom_point(aes(color = Count))
#Coexpression MOL5 and MOL6 vs MOL1
oligos.integratedOL256 <- subset(oligos.integrated,predicted.id %in% c("MOL2","MOL5","MOL6"))
Ptgdsexpression <- oligos.integratedOL256@assays$RNA@counts["Ptgds",]
Klk6expression <- oligos.integratedOL256@assays$RNA@counts["Klk6",]
ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
#plot(x=log(ExpressionCombo$Ptgds+1),y=log(ExpressionCombo$Klk6+1))
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(ExpressionCombosorted$Klk6)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = FALSE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(ExpressionCombosorted$Klk6)
namesMOL56 <- oligos.integratedOL256@meta.data$predicted.id
plot(x=ExpressionCombo$Ptgds,y=oligos.integratedOL256@meta.data$prediction.score.MOL5)
plot(x=ExpressionCombo$Ptgds,y=oligos.integratedOL256@meta.data$prediction.score.MOL6)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
MOL6pred <- oligos.integratedOL256@meta.data$prediction.score.MOL6
names(MOL6pred) <- row.names(oligos.integratedOL256@meta.data)
barplot(MOL6pred[row.names(ExpressionCombosorted)])
MOL5pred <- oligos.integratedOL256@meta.data$prediction.score.MOL5
names(MOL5pred) <- row.names(oligos.integratedOL256@meta.data)
barplot(MOL5pred[row.names(ExpressionCombosorted)])
MOL56pred <- rowMeans(cbind(MOL5pred,MOL6pred))
barplot(MOL56pred[row.names(ExpressionCombosorted)])
plot(MOL56pred,ExpressionCombosorted$Ptgds)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = FALSE),]
MOL2pred <- oligos.integratedOL256@meta.data$prediction.score.MOL2
names(MOL2pred) <- row.names(oligos.integratedOL256@meta.data)
barplot(MOL2pred[row.names(ExpressionCombosorted)])
ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
pred <- (MOL56pred)-(MOL2pred)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(pred[row.names(ExpressionCombosorted)])
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = FALSE),]
barplot(ExpressionCombosorted$Klk6)
barplot(pred[row.names(ExpressionCombosorted)])
ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
MOL56ID <- 1*oligos.integratedOL256@meta.data$predicted.id %in% c("MOL5","MOL6")
MOL2ID <- -1*oligos.integratedOL256@meta.data$predicted.id %in% c("MOL2")
MOLID <- MOL56ID+MOL2ID
names(MOLID) <- row.names(oligos.integratedOL256@meta.data)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(MOLID[row.names(ExpressionCombosorted)])
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = TRUE),]
barplot(ExpressionCombosorted$Klk6)
barplot(MOLID[row.names(ExpressionCombosorted)])
ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
MOLID <- droplevels(oligos.integratedOL256@meta.data$predicted.id)
names(MOLID) <- row.names(oligos.integratedOL256@meta.data)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
ExpressionCombosorted$ID <- MOLID[row.names(ExpressionCombosorted)]
ExpressionCombosorted$order <- seq_len(nrow(ExpressionCombosorted))
ggplot(ExpressionCombosorted, aes(x=order, fill=ID)) +
geom_density(alpha=0.4)
ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6","S100b","Opalin","Hopx","Apoe","Apod"),]))
MOLID <- droplevels(oligos.integratedOL256@meta.data$predicted.id)
names(MOLID) <- row.names(oligos.integratedOL256@meta.data)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = TRUE),]
ExpressionCombosorted$ID <- MOLID[row.names(ExpressionCombosorted)]
ExpressionCombosorted$order <- seq_len(nrow(ExpressionCombosorted))
ggplot(ExpressionCombosorted, aes(x=order, fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Ptgds+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Klk6+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(S100b+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Opalin+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Hopx+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Apoe+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Apod+1), fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Klk6+1),y=log(Ptgds+1), color=ID)) +
geom_point(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=Ptgds, fill=ID)) +
geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=Klk6, fill=ID)) +
geom_density(alpha=0.4)
---
title: "R Notebook"
output: html_notebook
---

```{r echo=FALSE}
library(Seurat)
library(ggplot2)
options(future.globals.maxSize = 4000 * 1024^2)
#Load data
paths <- c("/Volumes/Castelo-Branco/UPPMAX_INBOX_BACKUP/INBOX/10X_19_GCB_15_R42/10X_19_073/outs/filtered_feature_bc_matrix/",
           "/Volumes/Castelo-Branco/UPPMAX_INBOX_BACKUP/INBOX/10X_19_GCB_15_R42/10X_19_074/outs/filtered_feature_bc_matrix/",
           "/Volumes/Castelo-Branco/UPPMAX_INBOX_BACKUP/INBOX/10X_19_GCB_15_R42/10X_19_075/outs/filtered_feature_bc_matrix/")
names(paths) <- c("IS","CTRL","WD")
SC <- Read10X(data.dir = paths, gene.column = 2, unique.features = TRUE)
Samples <- strsplit(unlist(SC@Dimnames[2]),"_")
Samples <- unlist(lapply(Samples,function(x) x[1]))
names(Samples) <- colnames(SC)
table(Samples) 
emat_10x <- SC
anno_10x <- Samples
anno_10x <- as.data.frame(anno_10x,stringsAsFactors = FALSE)

colnames(anno_10x) <- "Sample"
#Put in Seurat object and split in two to perform prepnormalization
oligos <- CreateSeuratObject(emat_10x, meta.data =  anno_10x,min.cells = 3, min.features = 200)
```
```{r echo=TRUE, fig.height=6, fig.width=6}
# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
oligos[["percent.mt"]] <- PercentageFeatureSet(oligos, pattern = "^mt-")
# Visualize QC metrics as a violin plot
VlnPlot(oligos, group.by = "Sample",features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 1,pt.size = 0.1)
```
```{r echo=TRUE, fig.height=6, fig.width=10}
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(oligos, group.by = "Sample",feature1 = "nCount_RNA", feature2 = "percent.mt",pt.size = 0.5)
plot2 <- FeatureScatter(oligos, group.by = "Sample", feature1 = "nCount_RNA", feature2 = "nFeature_RNA",pt.size = 0.5)
CombinePlots(plots = list(plot1, plot2))
```
```{r}
#Clean up the data
oligos <- subset(oligos, subset = nFeature_RNA > 500 & nFeature_RNA < 7000 & percent.mt < 10)
ncol(oligos)
```
```{r echo=TRUE, fig.height=6, fig.width=6}
# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
oligos[["percent.mt"]] <- PercentageFeatureSet(oligos, pattern = "^mt-")
# Visualize QC metrics as a violin plot
VlnPlot(oligos, group.by = "Sample",features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 1,pt.size = 0.1)
```
```{r echo=TRUE, fig.height=6, fig.width=10}
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(oligos, group.by = "Sample",feature1 = "nCount_RNA", feature2 = "percent.mt",pt.size = 0.5)
plot2 <- FeatureScatter(oligos, group.by = "Sample", feature1 = "nCount_RNA", feature2 = "nFeature_RNA",pt.size = 0.5)
CombinePlots(plots = list(plot1, plot2))
```
Now we normalize the dataset.
```{r message=FALSE, warning=FALSE, include=FALSE, paged.print=FALSE}
#oligos.integrated <- SCTransform(oligos,verbose = FALSE)
# oligos.list <- SplitObject(oligos, split.by = "Sample")
# for (i in 1:length(oligos.list)) {
#     oligos.list[[i]] <- SCTransform(oligos.list[[i]], verbose = FALSE)
# }
# oligos.integrated <- merge(oligos.list[[1]],oligos.list,merge.data = TRUE)
#use code below when no integration is needed
oligos.integrated<- SCTransform(oligos, verbose = FALSE)
```
```{r eval=FALSE, include=FALSE}
#integrate
oligos.features <- SelectIntegrationFeatures(object.list = oligos.list, nfeatures = 3000)
oligos.list <- PrepSCTIntegration(object.list = oligos.list, anchor.features = oligos.features, 
    verbose = FALSE)
oligos.anchors <- FindIntegrationAnchors(object.list = oligos.list, normalization.method = "SCT", 
    anchor.features = oligos.features, verbose = FALSE)
oligos.integrated <- IntegrateData(anchorset = oligos.anchors, normalization.method = "SCT", 
    verbose = FALSE)
```
Generating the UMAP and TSNE.
```{r}
#DefaultAssay(oligos.integrated) <- "integrated"
oligos.integrated <- RunPCA(oligos.integrated, verbose = FALSE)
oligos.integrated <- RunUMAP(oligos.integrated, dims = 1:30)
#oligos.integrated <- RunTSNE(oligos.integrated, dims = 1:30)
plots <- DimPlot(oligos.integrated, group.by = c("Sample"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, 
    byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
# plots <- TSNEPlot(oligos.integrated, group.by = c("Sample"), combine = FALSE)
# plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, 
#     byrow = TRUE, override.aes = list(size = 3))))
# CombinePlots(plots)
```  
#### Label transfer
Now we attempt to transfer the cluster labels of the Science dataset onto the 10X dataset.
```{r message=FALSE, warning=FALSE, include=FALSE, paged.print=TRUE}
load("~/Documents/SingleCellData/Sciencedataset/Sciencematricesanno.Rdata")
anno_science$Sample <- rep("Science",ncol(emat_science))
Science <- CreateSeuratObject(emat_science, meta.data =  anno_science,min.cells = 3, min.features = 200)
Science <- SCTransform(Science, min_cells=3,verbose = FALSE)
#oligos.integrated <- Science
DefaultAssay(oligos.integrated) <- "SCT"

oligos.anchors <- FindTransferAnchors(reference = Science, query =oligos.integrated, dims = 1:30,project.query = T) 
predictions <- TransferData(anchorset = oligos.anchors, refdata = Science$cell_class, dims = 1:30)
oligos.integrated <- AddMetaData(oligos.integrated, metadata = predictions)
DimPlot(oligos.integrated, group.by = c("predicted.id"), combine = FALSE)

length(which(oligos.integrated$prediction.score.max < 0.3))
confidentclusters <- oligos.integrated$predicted.id
confidentclusters[which(oligos.integrated$prediction.score.max < 0.3)] <- "NA"
predictions <-cbind(predictions,confidentclusters)
oligos.integrated <- AddMetaData(oligos.integrated, metadata = predictions)
```
```{r}
oligos.integrated <- FindNeighbors(oligos.integrated, dims = 1:30)
oligos.integrated <- FindClusters(oligos.integrated,algorithm = 4,resolution = 0.6)
#0.6
```
```{r}
oligos.integrated$predicted.id <- factor(oligos.integrated$predicted.id,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
DimPlot(oligos.integrated, group.by = c("seurat_clusters"), combine = FALSE)
DimPlot(oligos.integrated, group.by = c("predicted.id"), combine = FALSE)
DimPlot(oligos.integrated, group.by = c("Sample"), combine = FALSE)
```

```{r}
table(oligos.integrated$Sample,oligos.integrated$predicted.id)
```
```{r}
data <- as.data.frame(table(oligos.integrated$Sample,droplevels(as.factor(oligos.integrated$predicted.id))))
colnames(data) <- c("Condition","Cluster","Freq")
library(plyr)
data$Cluster  <- factor(data$Cluster,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
data <- data[which(data$Cluster %in% c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6")),]
#data$Cluster  <- factor(data$Cluster,levels=c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6"))
library(reshape2)
datacasted <- dcast(data,Cluster ~ Condition)
calc_cpm <-function (expr_mat) 
{
    norm_factor <- colSums(expr_mat)
    return(t(t(expr_mat)/norm_factor))
}
datacasted[,2:4] <- calc_cpm(datacasted[,2:4])
data <- melt(datacasted)
colnames(data) <- c("Condition","Cluster","Freq")
#data$Cluster  <- revalue(as.factor(data$Cluster),c("PPR"="VLMC"))
# Stacked + percent
ggplot(data, aes(fill=Condition, y=Freq, x=Cluster)) + 
    geom_bar(position="fill", stat="identity")
ggplot(data, aes(fill=Cluster, y=Freq, x=Condition)) + 
    geom_bar( stat="identity")

row.names(datacasted) <- datacasted[,1]
datacasted <- datacasted[,2:4]*100
datamelted <- melt(t(datacasted))

ggplot(datamelted, aes(y = value, x = Var2)) + # Move y and x here so than they can be used in stat_*
    geom_dotplot(aes(fill = Var1),   # Use fill = Species here not in ggplot()
                 binaxis = "y",         # which axis to bin along
                 binwidth = 1,        # Minimal difference considered diffeerent
                 stackdir = "center",
                 position = position_jitter(0.2)# Centered
                 ) +  # scale_y_log10() + 
    stat_summary(fun.y = mean, fun.ymin = mean, fun.ymax = mean,
                 geom = "crossbar", width = 0.5,fatten = 0.01) + theme(axis.text.x = element_text(angle = 45))
```
```{r fig.height=3, fig.width=3}
library(heatmap3)
library(viridis)
data <- as.data.frame(table(oligos.integrated$Sample,droplevels(as.factor(oligos.integrated$predicted.id))))
colnames(data) <- c("Condition","Cluster","Freq")
library(plyr)
data$Cluster  <- factor(data$Cluster,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
data <- data[which(data$Cluster %in% c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6")),]
#data$Cluster  <- factor(data$Cluster,levels=c("MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6"))
library(reshape2)
datacasted <- dcast(data,Cluster ~ Condition)
calc_cpm <-function (expr_mat) 
{
    norm_factor <- colSums(expr_mat)
    return(t(t(expr_mat)/norm_factor))
}
datacasted[,2:4] <- calc_cpm(datacasted[,2:4])*100
row.names(datacasted) <- datacasted[,1]
datacasted <- datacasted[,2:4]
comparison <-datacasted-apply(datacasted,1,function(x) mean(x))
comparison <-datacasted-datacasted[,1]
heatmap3(comparison[rev(row.names(comparison)),], Rowv = NA , Colv = NA ,scale = "none",symm = F, method = "ward.D2",col=viridis(1000),balanceColor =F,cexRow = 1,cexCol = 1,margins = c(10, 10))
library(RColorBrewer)
heatmap3(comparison[rev(row.names(comparison)),], Rowv = NA , Colv = NA ,scale = "none",symm = F, method = "ward.D2",col=rev(colorRampPalette(brewer.pal(1024,"RdBu"))(1024)),balanceColor =T,cexRow = 1,cexCol = 1,margins = c(10, 10))
  relationshipratio <- cor(t(comparison),method="pearson")
heatmap3(relationshipratio[rev(row.names(comparison)),], Rowv = NULL , Colv = NULL ,scale = "none",symm = F, method = "ward.D2",col=colorRampPalette(c("limegreen","black",
"firebrick3"))(1024),balanceColor =F,cexRow = 2,cexCol = 2,margins = c(10, 10))
```
```{r}
barplot(table(oligos.integrated$Sample,oligos.integrated$predicted.id))
data <- as.data.frame(table(oligos.integrated$Sample,oligos.integrated$predicted.id))
colnames(data) <- c("Condition","Cluster","Freq")
library(plyr)
data$Cluster  <- factor(data$Cluster,levels=c("OPC","COP","NFOL1","MFOL1","MFOL2","MOL1","MOL2","MOL3","MOL4","MOL5","MOL6","PPR"))
data$Cluster  <- revalue(as.factor(data$Cluster),c("PPR"="VLMC"))
dataIS_CTRL <- data[which(data$Condition %in% c("IS","CTRL")),]
dataWD_CTRL <- data[which(data$Condition %in% c("WD","CTRL")),]
dataIS_WD <- data[which(data$Condition %in% c("IS","WD")),]
# Stacked + percent
ggplot(data, aes(fill=Condition, y=Freq, x=Cluster)) + 
    geom_bar(position="fill", stat="identity")
ggplot(dataIS_CTRL, aes(fill=Condition, y=Freq, x=Cluster)) + 
    geom_bar(position="fill", stat="identity")
ggplot(dataWD_CTRL, aes(fill=Condition, y=Freq, x=Cluster)) + 
    geom_bar(position="fill", stat="identity")
ggplot(dataIS_WD, aes(fill=Condition, y=Freq, x=Cluster)) + 
    geom_bar(position="fill", stat="identity")
```

```{r}
#subset OPCs
oligos.integratedIm <- subset(oligos.integrated,seurat_clusters %in% c(12,11,8,9))


oligos.integratedIm <- FindVariableFeatures(oligos.integratedIm)
DefaultAssay(oligos.integratedIm) <- "SCT"
```


#Spatially filter genes with auto bootstrapping
#Script will give error when running out of genes, this is normal
```{r, message=FALSE}
networkExpressionFile=oligos.integratedIm@assays$SCT@scale.data
expr_limit <- min(networkExpressionFile)-0.01
featureselection <- row.names(networkExpressionFile)
gc()
library(umap)
useUMAP <- "TRUE"
UMAPdim <- 3
adjustforbias <- "FALSE"
GeneMarkovList <- list()
GeneFilterProgression <- as.data.frame(featureselection)
row.names(GeneFilterProgression) <- featureselection
corenumber <- 4
usemagic <- "FALSE"
pcathresh <- 20
ncompGF <- 20
whentobootstrap <- 20000 #cellnumber
howmanyboots <- 10+1 #bootstrap repetitions
samplesize <- 2000 #how many cells to take for sampling
threshold <- 3 #use mean spatial correlation of geneset of the featureselection (1) or after the first round of filtering (2) (more strict, which is default) 
nsim <- 100
nnquant <- 0.995 #lower for small datasets, higher for speedups in larger datasets

tri.to.squ<-function(x)
{
rn<-row.names(x)
cn<-colnames(x)
an<-unique(c(cn,rn))
myval<-x[!is.na(x)]
mymat<-matrix(1,nrow=length(an),ncol=length(an),dimnames=list(an,an))
for(ext in 1:length(cn))
{
 for(int in 1:length(rn))
 {
 if(is.na(x[row.names(x)==rn[int],colnames(x)==cn[ext]])) next
 mymat[row.names(mymat)==rn[int],colnames(mymat)==cn[ext]]<-x[row.names(x)==rn[int],colnames(x)==cn[ext]]
 mymat[row.names(mymat)==cn[ext],colnames(mymat)==rn[int]]<-x[row.names(x)==rn[int],colnames(x)==cn[ext]]
 }
}
return(mymat)
}

SCN3Egeneset <- featureselection
originalnetworkdf <- networkExpressionFile
if(ncol(networkExpressionFile)>whentobootstrap){
originalnetworkdf <- networkExpressionFile
bootstrap<-1
sample<-1
moransIsampled <- as.character(NULL)
}
if(ncol(networkExpressionFile)<=whentobootstrap)
{
  bootstrap<-howmanyboots-1
  sample<-0
  }
while(bootstrap<howmanyboots){
SCN3Egeneset <- featureselection
iter=0
meanMoran<-0
if(sample==1){
networkExpressionFile  <- originalnetworkdf[,sample(ncol(originalnetworkdf), samplesize) ]
print(paste("bootstrapping...","round",bootstrap))
}
OldGeneset <- as.character(seq_len(100000))
while(length(OldGeneset) > (length(SCN3Egeneset)+10) ){ # uncomment this and below to enable iterative filtering
 iter=iter+1
  #library(amap)
#library(MASS)
#library(destiny)
#library(dpt)
#library(Matrix)
#library(diffusionMap)
print(paste("Preparing genefiltering on", length(SCN3Egeneset), "genes."))
#print("Making celllandscape...Filtering possible duplicated cells in original file")
#if(length(OldGeneset)==100000)
#{
#  pca <- prcomp(t(log(networkExpressionFile[featureselection,]+1)),scale. = FALSE)
#  cd_diffusionplot <-pca$x[,c(1:30)]
#  }

#if(length(OldGeneset)!=100000){cd_diffusionplot <- t(log(networkExpressionFile[SCN3Egeneset,]+1))}
  if(length(SCN3Egeneset) > pcathresh){
#library(rsvd)
    print("Making celllandscape...Performing dimensionality reduction")
#pca <- rpca(t(log(networkExpressionFile[SCN3Egeneset,]+1)))
#pca <- prcomp(t(log(networkExpressionFile[SCN3Egeneset,]+1)),scale. = TRUE)
pca <- prcomp(t(networkExpressionFile[SCN3Egeneset,]),scale. = TRUE)
if(length(SCN3Egeneset) < 100) {ncompGF <- length(SCN3Egeneset)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.8)[1])]
  }
   if(length(SCN3Egeneset) <= pcathresh){
#cd_diffusionplot <-t(log(networkExpressionFile[SCN3Egeneset,]+1))
cd_diffusionplot <-t(networkExpressionFile[SCN3Egeneset,])
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
print("Making celllandscape...Making diffusionmap")
# gc()
#ts <- Transitions(cd_diffusionplot,k=20)
if(useUMAP==TRUE)
{
  library(umap)
umap.settings <- umap.defaults
umap.settings$n_neighbors <-10
umap.settings$min_dist <- 0
umap.settings$n_components <- UMAPdim
umap.settings$metric <- "manhattan"
#umap.settings$local_connectivity <- 0.00001
umap.settings$n_epochs <- 1000
umap_out <- umap(cd_diffusionplot,config = umap.settings,method="umap-learn")
}
if(useUMAP != TRUE){
library(destiny)
ts <- DiffusionMap(cd_diffusionplot, verbose = TRUE)
}
if(adjustforbias==TRUE){
testwilcox <- as.matrix(apply(t(ts@eigenvectors),1,function(x) apply(tri.to.squ(pairwise.wilcox.test(x,biasedfactor,p.adjust.method = "fdr")$p.value),2,function(x) min(x,na.rm=TRUE))))

testwilcoxdifference <- scale(t(testwilcox))
biasedcomponents <- unique(unlist(apply(testwilcoxdifference,2,function(x) which(abs(x) > 1.96))))
}
#Gene filtering
print(paste("Filtering", length(SCN3Egeneset), "genes."))
OldGeneset <- SCN3Egeneset
#NetworkDist <- as.matrix(ts@transitions)
library(amap)
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
if(useUMAP==TRUE)
  {
  if(adjustforbias==TRUE){
NetworkDist <- 1-range01((as.matrix(Dist(umap_out$layout[,1:UMAPdim],method = "manhattan",nbproc = 8))))}
else{NetworkDist <- 1-range01((as.matrix(Dist(umap_out$layout[,1:UMAPdim],method = "manhattan",nbproc = 8))))}
}
if(useUMAP != TRUE){
if(adjustforbias==TRUE){
NetworkDist <- 1-range01((as.matrix(Dist(ts@eigenvectors[,-c(biasedcomponents)],method = "manhattan",nbproc = 8))))}
else{NetworkDist <- 1-range01((as.matrix(Dist(ts@eigenvectors,method = "manhattan",nbproc = 8))))}
}
#NetworkDist <- distcells*distcells2
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
emat_expressed <- apply(networkExpressionFile,1,function(x) any ((x) >expr_limit))
emat_expressed <- networkExpressionFile[emat_expressed,row.names(NetworkDist)]
emat_expressed <- as.matrix(emat_expressed[intersect(row.names(emat_expressed),OldGeneset),])
if(usemagic=="TRUE"){
emat_expressed <- magic(ts,emat_expressed[,row.names(cd_diffusionplot)], power = 1, k = 20, n_eigs = 20, n_local = 10)
}
#library(doParallel)
#library(foreach)
library(parallel)
library(spdep)
cores <- corenumber
# cl <- makeCluster(cores)  
# registerDoParallel(cl)  
SCN3Egenefilter <- matrix(nrow=nrow(emat_expressed),ncol = 3) 
cellnames <- colnames(emat_expressed)
# library(Matrix)
# NetworkDistsparse <- Matrix(NetworkDist, sparse = TRUE)
print("Making celllandscape...Generating Spatial Weights Matrix...")
i=1
NetworkDist2 <- matrix(nrow=nrow(NetworkDist),ncol=ncol(NetworkDist))
  for(i in 1:ncol(NetworkDist2))
    {
  x <-NetworkDist[,i] 
 x[x < as.numeric(quantile(x,nnquant))] <- 0
 NetworkDist2[,i] <- x
  }
NetworkDist <- NetworkDist2
rm(NetworkDist2)
colnames(NetworkDist) <- row.names(cd_diffusionplot)
row.names(NetworkDist) <- row.names(cd_diffusionplot)
spatial.weights <- mat2listw(NetworkDist[])
print("Making celllandscape...Generating Spatial Weights Matrix...done")
numbsim <- nsim
i=1
test <- as.data.frame(mclapply(1:nrow(emat_expressed), function(i) {
   return(m <- c(unlist(moran.mc(emat_expressed[i,],spatial.weights,nsim=numbsim,zero.policy = TRUE))[1:3],row.names(emat_expressed)[i]))
}, mc.cores=corenumber))


SCN3Egenefilter <- t(test)
SCN3Egenefilter <- SCN3Egenefilter[! apply(SCN3Egenefilter,1,function(x) any(x=="NaN")),]
row.names(SCN3Egenefilter) <- SCN3Egenefilter[,4]
SCN3Egenefilter_clean <- as.data.frame(SCN3Egenefilter[complete.cases(SCN3Egenefilter),c(1:4)])
 r <- row.names(SCN3Egenefilter_clean)
SCN3Egenefilter_clean <- apply(SCN3Egenefilter_clean,2,function(x) as.numeric(x))
row.names(SCN3Egenefilter_clean) <- r
SCN3Egeneset <- row.names(subset(SCN3Egenefilter_clean,SCN3Egenefilter_clean[,3] <= 0.01))
HSEgenes <- row.names(subset(SCN3Egenefilter_clean,SCN3Egenefilter_clean[,3] <= 0.01 & SCN3Egenefilter_clean[,1] >= as.numeric(quantile(SCN3Egenefilter_clean[SCN3Egeneset,1],0.1))))
SCN3Egeneset <- row.names(subset(SCN3Egenefilter_clean,SCN3Egenefilter_clean[,3] <= 0.01 & SCN3Egenefilter_clean[,1] >= as.numeric(quantile(SCN3Egenefilter_clean[SCN3Egeneset,1],0.1))))
GeneFilterProgression <- cbind(GeneFilterProgression[SCN3Egeneset,],SCN3Egenefilter_clean[SCN3Egeneset,1])
if(iter<=threshold){meanMoran<-mean(abs(SCN3Egenefilter_clean[,1]),na.rm=TRUE)}
GeneMarkovList <- c(list(GeneMarkov=SCN3Egeneset),GeneMarkovList)
} #uncomment this to enable iterative filtering
bootstrap <- bootstrap+1
if(sample==1){
moransIsampled <- c(moransIsampled,SCN3Egeneset)
}
}
networkExpressionFile <- originalnetworkdf
GeneMarkov <- SCN3Egeneset
if(sample==1){GeneMarkov <- unique(moransIsampled)}
#L1geneset <- GeneMarkov
rm(emat_expressed,NetworkDist,SCN3Egenefilter,test,cellnames,cl,cores,i,iter,meanMoran,numbsim,OldGeneset,r,SCN3Egeneset,spatial.weights)
L1geneset <- GeneMarkov
rm(emat_expressed,NetworkDist,SCN3Egenefilter,test,cellnames,cl,cores,i,iter,meanMoran,numbsim,OldGeneset,r,SCN3Egeneset,spatial.weights)

  print("Making final celllandscape...Filtering possible duplicated cells in original file")
   if(length(GeneMarkov) > pcathresh){
pca <- prcomp(t(log(networkExpressionFile[GeneMarkov,]+1)),scale. = FALSE)

if(length(GeneMarkov) < 100) {ncompGF <- length(GeneMarkov)}
cd_diffusionplot <-pca$x[,c(1:which(cumsum(pca$sdev[1:ncompGF])/sum(pca$sdev[1:ncompGF]) > 0.8)[1])]
  }
   if(length(GeneMarkov) <= pcathresh){
cd_diffusionplot <-t(log(networkExpressionFile[GeneMarkov,]+1))
}
cd_diffusionplot <- cd_diffusionplot[!duplicated(cd_diffusionplot),]
if(useUMAP != TRUE){
print("Making final celllandscape...Making diffusionmap. This can take a while...")
library(destiny)
ts <- DiffusionMap(cd_diffusionplot, verbose = TRUE)
}
  if(usemagic=="TRUE"){
    networkExpressionFilepremagic <-  networkExpressionFile
networkExpressionFile <- magic(ts,networkExpressionFile[,row.names(cd_diffusionplot)], power = 1, k = 20, n_eigs = 20, n_local = 10)
  }
if(useUMAP==TRUE)
{
  print("Making final celllandscape...Making UMAP. This can take a while...")
umap.settings <- umap.defaults
umap.settings$n_neighbors <-4
umap.settings$min_dist <- 0
umap.settings$n_components <-UMAPdim
umap.settings$metric <- "manhattan"
#umap.settings$local_connectivity <- 0.00001
umap.settings$n_epochs <- 1000
umap_out <- umap(cd_diffusionplot,config = umap.settings,method="umap-learn")
}
print("Done.")
if(adjustforbias==TRUE){
testwilcox <- as.matrix(apply(t(ts@eigenvectors),1,function(x) apply(tri.to.squ(pairwise.wilcox.test(x,biasedfactor,p.adjust.method = "fdr")$p.value),2,function(x) min(x,na.rm=TRUE))))

testwilcoxdifference <- scale(t(testwilcox))
biasedcomponentswilcox <- unique(unlist(apply(testwilcox,2,function(x) which(abs(x) ==0))))
biasedcomponents <- unique(unlist(apply(testwilcoxdifference,2,function(x) which(abs(x) > 1.96))))
}
```
```{r}
length_iterations <-  unlist(lapply(GeneMarkovList,function(x) length(x)))
plot(seq_along(length_iterations),length_iterations)
```
```{r}
pcatsne <- prcomp(t(as.matrix(networkExpressionFile[unlist(GeneMarkovList[26]),])),scale. = TRUE)
pca <- pcatsne
plot(log(pcatsne$sdev[1:50]),pch = 20,
xlab = 'Principal component', ylab = 'sdev')
ncompL1tsne <- which(cumsum(pca$sdev[1:50])/sum(pca$sdev[1:50]) > 0.6)[1]
tsne <- t(pca$x[,c(1:ncompL1tsne)]) 

number_of_clusters <- 8
NetworkDist <- Dist(t(tsne),method = "manhattan",nbproc = 8)
GeneMarkov_hc <- hclust(NetworkDist, method = "ward.D2")
GMcluster <- rbind(groups = cutree(GeneMarkov_hc, k=number_of_clusters))
names(GMcluster) <- colnames(networkExpressionFile)
oligos.integratedIm$seurat_clusters <- as.factor(GMcluster)
GeneMarkov  <- unlist(GeneMarkovList[27])
```
```{r}
DefaultAssay(oligos.integratedIm) <- "SCT"
oligos.integratedIm <- RunPCA(oligos.integratedIm, verbose = FALSE,features = GeneMarkov)#npcs = 20)
ElbowPlot(oligos.integratedIm)
```
```{r}
oligos.integratedIm <- RunUMAP(oligos.integratedIm, dims = 1:10)
#oligos.integratedScience <- RunTSNE(oligos.integratedScience, dims = 1:10)
plots <- DimPlot(oligos.integratedIm, group.by = c("seurat_clusters"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, 
    byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
plots <- DimPlot(oligos.integratedIm, group.by = c("Sample"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, 
    byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
```  
```{r include=FALSE}
# find markers for every cluster compared to all remaining cells, report only the positive ones
Idents(oligos.integratedIm) <- "seurat_clusters"
library(dplyr)
oligos.integrated.markersIm <- FindAllMarkers(oligos.integratedIm, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
```
```{r}
oligos.integrated.markersIm %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)
```
```{r fig.width=10}
library(viridis)
DefaultAssay(oligos.integratedIm) <- "SCT"
top10 <- oligos.integrated.markersIm %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
# DoHeatmap(oligos.integratedIm, features = intersect(oligos.integrated.markersIm$gene,GeneMarkov),disp.max=7,) + NoLegend() +scale_fill_viridis()

DoHeatmap(oligos.integratedIm, features =intersect(oligos.integrated.markersIm$gene,unique(c(unlist(GeneMarkovList[5]),top10$gene))),disp.max=3) + NoLegend() +scale_fill_viridis()
```
```{r}
#for MT=10%
oligos.integratedOL <- subset(oligos.integrated,seurat_clusters %in% c(1:6,8,9))
#non-integrated
#oligos.integratedOL <- subset(oligos.integrated,seurat_clusters %in% c(1:6,10))
#for MT=5%
#oligos.integratedOL <- subset(oligos.integrated,seurat_clusters %in% c(1:5))
DefaultAssay(oligos.integrated) <- "SCT"
# oligos.integratedOL <- RunPCA(oligos.integratedOL, verbose = FALSE,features=c("Opalin","Ptgds","Apoe","S100b","Apod","Lamp1","Fos","Sepp1"),npcs=5,approx=FALSE)
oligos.integratedOL <- RunPCA(oligos.integratedOL, verbose = FALSE)#,features=GeneMarkov)
ElbowPlot(oligos.integratedOL)
```
```{r}
oligos.integratedOL <- RunUMAP(oligos.integratedOL, dims = 1:30)
#oligos.integratedScience <- RunTSNE(oligos.integratedScience, dims = 1:10)
plots <- DimPlot(oligos.integratedOL, group.by = c("seurat_clusters"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, 
    byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)
plots <- DimPlot(oligos.integratedOL, group.by = c("Sample"), combine = FALSE)
plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, 
    byrow = TRUE, override.aes = list(size = 3))))
CombinePlots(plots)

DimPlot(oligos.integratedOL, group.by = c("Sample"), combine = FALSE)
DimPlot(oligos.integratedOL, group.by = c("seurat_clusters"), combine = FALSE)
DimPlot(oligos.integratedOL, group.by = c("predicted.id"), combine = FALSE,label=TRUE)
```  
```{r include=FALSE}
DefaultAssay(oligos.integratedOL) <- "SCT"
# find markers for every cluster compared to all remaining cells, report only the positive ones
Idents(oligos.integratedOL) <- "predicted.id"
library(dplyr)
oligos.integrated.markersOL <- FindAllMarkers(oligos.integratedOL, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.1)
```
```{r}
oligos.integrated.markersOL %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)
```

```{r fig.width=10}
DefaultAssay(oligos.integratedOL) <- "SCT"
# Normalize RNA data for visualization purposes
#oligos.integrated <- NormalizeData(oligos.integrated, verbose = FALSE)
FeaturePlot(oligos.integratedOL, c("Pdgfra", "Ptprz1","Bmp4","Itpr2", "Egr1","Egr2", "Fos","Klk6", "Hopx", "Ptgds","Il33","Mbp","Cd74","Serpina3n"),pt.size = 1)

FeaturePlot(oligos.integratedOL, c("Opalin","Ptgds","Apoe","S100b","Apod","Lamp1","Fos","Sepp1","Klk6","Hopx"),pt.size = 1)
DefaultAssay(oligos.integrated) <- "SCT"
```

```{r}
library(ggrepel)
DefaultAssay(oligos.integratedOL) <- "SCT"
Idents(oligos.integratedOL) <- "Sample"
oligos.integrated.samplediffIS <- FindMarkers(oligos.integratedOL, ident.1 = "IS", ident.2 = "CTRL", verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffIS
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.7),                              label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.7)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 

oligos.integrated.samplediffWD <- FindMarkers(oligos.integratedOL, ident.1 = "WD", ident.2 = c("CTRL"), verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffWD
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.35),                              label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.35)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 

oligos.integrated.samplediffISWD <- FindMarkers(oligos.integratedOL, ident.1 = "IS", ident.2 = "WD", verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffISWD
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85),                              label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 

oligos.integrated.samplediffISWDvsCNTRL <- FindMarkers(oligos.integratedOL, ident.1 = c("IS","WD"), ident.2 = "CTRL", verbose = FALSE,logfc.threshold = 0,min.pct=0)
#head(oligos.integrated.samplediffAllRNA, n = 50)
diffmatrix <- oligos.integrated.samplediffISWDvsCNTRL
diffmatrix$logp_val <- -log10(diffmatrix$p_val_adj)
ggplot(diffmatrix,aes(avg_logFC,y=logp_val,label=row.names(diffmatrix)))+ geom_point(size=0.5)+ geom_text_repel(data=subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85),                              label=row.names(subset(diffmatrix, p_val_adj < 0.01 & abs(avg_logFC) > 0.85)))+xlab("log2_FC") + ylab("-log10_p-value_adj") + geom_hline(yintercept=-log10(0.01),linetype="dashed",size=0.5) 
DefaultAssay(oligos.integrated) <- "SCT"
```
```{r}
DiffMatrix <- list()

diffmatrixnames <- c("oligos.integrated.samplediffISWD",
                    "oligos.integrated.samplediffISWDvsCNTRL")
                     

do.call(head,as.list(as.name(diffmatrixnames[1])))
```
```{r}
DiffMatrix <- list()

diffmatrixnames <- c("oligos.integrated.samplediffISWD",
                    "oligos.integrated.samplediffISWDvsCNTRL")
                     

do.call(head,as.list(as.name(diffmatrixnames[1])))
```
```{r}
library(clusterProfiler)
#Convert to gencode using biomart
library(biomaRt)
listMarts()
ensembl = useMart("ensembl",dataset="mmusculus_gene_ensembl")
listDatasets(ensembl)
attributes = listAttributes(ensembl)
Biomart_gencode_ensembl84_biotypes <- getBM(attributes=c("mgi_symbol","ensembl_gene_id","entrezgene_id","gene_biotype"), filters = "", values = "", ensembl)
Biomart_gencode_ensembl84_biotypes[, 'gene_biotype'] <- as.factor(Biomart_gencode_ensembl84_biotypes[,'gene_biotype'])
#Filter for only our genes
 Biotype_All_dataset <- subset(Biomart_gencode_ensembl84_biotypes, mgi_symbol %in% oligos.integrated@assays$SCT@var.features)
entrezID <-  subset(Biotype_All_dataset, Biotype_All_dataset$mgi_symbol %in% oligos.integrated@assays$SCT@var.features)
```
```{r}
# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# 
# BiocManager::install("reactome.db")
library(ReactomePA)
library(org.Mm.eg.db)
ReactomeTerms <- list()
i=1
#UP
pvaladj <- 0.01
logfc <- 0.25
for(i in 1:length(diffmatrixnames)){
diffmatrix <- do.call("as.data.frame",as.list(as.name(diffmatrixnames[i])))
diffmatrix <- subset(diffmatrix, p_val_adj < pvaladj & avg_logFC > logfc)
siggenes <- head(row.names(diffmatrix),50)
entrezmatched <- entrezID[entrezID$mgi_symbol %in% siggenes,]
#entrezID <- entrezID[! apply(entrezID[,c(1,3)], 1,function (x) anyNA(x)),]
allLLIDs <- entrezmatched$entrezgene
modulesReactome <- enrichPathway(gene=allLLIDs,organism="mouse",pvalueCutoff=0.01,qvalueCutoff = 0.3,pAdjustMethod = "none", readable=T)
ReactomeTerms[[i]] <- modulesReactome
head(as.data.frame(modulesReactome))
print(i)
}
ReactomeTerms[which(lapply(ReactomeTerms,function(x) is.null(x))==TRUE)] <- "No_Genes"

#Add DOWN 
pvaladj <- 0.01
logfc <- -0.25
offset <- length(ReactomeTerms)
for(i in 1:length(diffmatrixnames)){
  i=i+offset
diffmatrix <- do.call("as.data.frame",as.list(as.name(diffmatrixnames[i-offset])))
diffmatrix <- subset(diffmatrix, p_val_adj < pvaladj & avg_logFC < logfc)
siggenes <- head(row.names(diffmatrix),50)
entrezmatched <- entrezID[entrezID$mgi_symbol %in% siggenes,]
#entrezID <- entrezID[! apply(entrezID[,c(1,3)], 1,function (x) anyNA(x)),]
allLLIDs <- entrezmatched$entrezgene
modulesReactome <- enrichPathway(gene=allLLIDs,organism="mouse",pvalueCutoff=0.01,qvalueCutoff = 0.3,pAdjustMethod = "none", readable=T)
ReactomeTerms[[i]] <- modulesReactome
head(as.data.frame(modulesReactome))
print(i)
}
ReactomeTerms[which(lapply(ReactomeTerms,function(x) is.null(x))==TRUE)] <- "No_Genes"
```

```{r}
Upper_diff <- subset(oligos.integrated.samplediffISWD, p_val_adj < 0.01 & abs(avg_logFC) > 0)
Lower_diff <- subset(oligos.integrated.samplediffISWDvsCNTRL, p_val_adj < 0.01 & abs(avg_logFC) > 0)
AlldiffgenesHetMOL5 <- intersect(intersect(row.names(oligos.integrated.samplediffISWD),row.names(oligos.integrated.samplediffISWDvsCNTRL)),unique(c(row.names(Upper_diff),row.names(Lower_diff))))
subset2 <- oligos.integrated.samplediffISWD[AlldiffgenesHetMOL5,]
subset3 <- oligos.integrated.samplediffISWDvsCNTRL[AlldiffgenesHetMOL5,]
subsetMOL5 <- cbind(subset2,subset3)
colnames(subsetMOL5) <- make.unique(colnames(subsetMOL5))
diffmatrix <- subsetMOL5
diffmatrix$log_p_val <- -log10(diffmatrix$p_val_adj)
q95pgenes1 <- row.names(diffmatrix[which(diffmatrix$log_p_val >= quantile(diffmatrix$log_p_val,0)),])
diffmatrix$log_p_val.1 <- -log10(diffmatrix$p_val_adj.1)
q95pgenes2 <- row.names(diffmatrix[which(diffmatrix$log_p_val.1 >= quantile(diffmatrix$log_p_val.1,0)),])
q95pgenes <- unique(c(q95pgenes1,q95pgenes2))
diffmatrix <- diffmatrix[q95pgenes,]
diffmatrix$avg_logFC[is.infinite(diffmatrix$avg_logFC)] <- max(diffmatrix$avg_logFC[!is.infinite(diffmatrix$avg_logFC)])
diffmatrix$avg_logFC.1[is.infinite(diffmatrix$avg_logFC.1)] <- max(diffmatrix$avg_logFC.1[!is.infinite(diffmatrix$avg_logFC.1)])
#diffmatrix$avg_logFC.1 <- 2*diffmatrix$avg_logFC.1
diffmatrix$combp <- -log10(diffmatrix$p_val_adj*diffmatrix$p_val_adj.1)
diffmatrix$maxp <- apply(cbind(diffmatrix$log_p_val,diffmatrix$log_p_val.1),1,function(x) max(x))
diffmatrix$minp <- apply(cbind(diffmatrix$p_val_adj,diffmatrix$p_val_adj.1),1,function(x) min(x))
diffmatrix$maxp[is.infinite(diffmatrix$maxp)] <- max(diffmatrix$maxp[!is.infinite(diffmatrix$maxp)])
diffmatrix$maxFC <- apply(cbind(diffmatrix$avg_logFC,diffmatrix$avg_logFC.1),1,function(x) max(abs(x))) 
diffmatrix$Genes <- factor(row.names(diffmatrix),levels=row.names(diffmatrix))

ggplot(diffmatrix,aes(avg_logFC,y=avg_logFC.1,colour=maxp,label=row.names(diffmatrix)))+ geom_point(size=diffmatrix$maxp/100) + scale_colour_viridis_c(direction = +1,option ="viridis" ) + geom_hline(yintercept= 0,linetype="dashed",size=0.1,color="cyan")+
  geom_hline(yintercept= 0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
  geom_hline(yintercept= -0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
  geom_vline(xintercept= 0,linetype="dashed",size=0.1,color="cyan")+
  geom_vline(xintercept= 0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
  geom_vline(xintercept= -0.25,linetype="dashed",size=0.1,color="grey",alpha=0.5)+
  geom_text_repel(size=3,fontface = "bold",force=1,data=subset(diffmatrix, 
maxp > quantile(diffmatrix$maxp,0.98) #| 
# avg_logFC > 0 |
# avg_logFC < -0|
# avg_logFC.1 > 0 |
# avg_logFC.1 < -0)
),label=row.names(subset(diffmatrix, 
maxp > quantile(diffmatrix$maxp,0.98) #| 
# avg_logFC > 0 |
# avg_logFC < -0 |
# avg_logFC.1 > 0 |
)# avg_logFC.1 < -0)
))+xlab("IS vs WD") + ylab("Other vs Control") +theme(
  # get rid of panel grids
  panel.grid.major = element_blank(),
  #panel.grid.major = element_line(color="darkgrey",size=0.1),
  panel.grid.minor = element_blank(),
  #panel.grid.minor = element_line(color="darkgrey",size=0.05),
  # Change plot and panel background
  plot.background=element_rect(fill = "white"),
  panel.background = element_rect(fill = 'black'),
  # Change legend 
  legend.background = element_rect(fill = "white", color = NA),
  legend.key = element_rect(color = "gray", fill = "white"),
  legend.title = element_text(color = "Black"),
  legend.text = element_text(color = "black")
  )
#magma,inferno, plasma,viridis
#scale_colour_gradient(low = "darkgreen", high = "red")
#Do reactome analysis at the bottom of script
i=1
j=1
#for(i in 1:length(ReactomeTerms)){
for(i in 1:4){
pwydata <- as.data.frame(ReactomeTerms[[i]])
geneset <- strsplit(pwydata$geneID, "/")
FCmeans <- data.frame()
for(j in 1:length(geneset)){
 geneset2FC <- which(row.names(diffmatrix) %in% geneset[[j]])
 FC <- mean(diffmatrix$avg_logFC[geneset2FC],na.rm=T)
 FCvar <- var(diffmatrix$avg_logFC[geneset2FC],na.rm=T)
 FC.1 <- mean(diffmatrix$avg_logFC.1[geneset2FC],na.rm=T)
 FC.1var <- var(diffmatrix$avg_logFC.1[geneset2FC],na.rm=T)
FCmeans <- rbind(FCmeans,cbind(FC,FC.1,FCvar,FC.1var))
 print(j)
}
ReactomeTerms[[i]] <- cbind(ReactomeTerms[[i]],FCmeans)
print(i)
}
pathmatrix <- rbind(as.data.frame(ReactomeTerms[[1]]),as.data.frame(ReactomeTerms[[2]]),as.data.frame(ReactomeTerms[[3]]),as.data.frame(ReactomeTerms[[4]]))
#pathmatrix <- rbind(as.data.frame(ReactomeTerms[[1]]),as.data.frame(ReactomeTerms[[2]]))
#pathmatrix <- rbind(as.data.frame(ReactomeTerms[[3]]),as.data.frame(ReactomeTerms[[4]]))

pathmatrix$p.adjust_original <- pathmatrix$p.adjust
pathmatrix$p.adjust <- -log10(pathmatrix$p.adjust )
pathmatrix$maxFC <- sum(abs(pathmatrix$FC),abs(pathmatrix$FC.1))
pathmatrix <- subset(pathmatrix, pathmatrix$Count > 1)
pathmatrix$AdjSelect <- pathmatrix$p.adjust*(500*(0.2+abs(pathmatrix$FC)))

#scale_colour_gradient(low = "yellow", high = "red") +
#scale_colour_viridis_c(direction = -1)
#scale_colour_gradient(low = "black", high = "red")
ggplot(pathmatrix,aes(FC,y=FC.1,colour=p.adjust_original),label=pathmatrix$Description)+ geom_point(size=pathmatrix$Count,alpha=0.5) +scale_colour_viridis_c(direction = +1,option = "viridis") +
  geom_hline(yintercept= 0,linetype="solid",size=0.5,color="black",alpha=0.5)+
  geom_hline(yintercept= 0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
  geom_hline(yintercept= -0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
  geom_vline(xintercept= 0,linetype="solid",size=0.5,color="black",alpha=0.5)+
  geom_vline(xintercept= 0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
  geom_vline(xintercept= -0.25,linetype="solid",size=0.2,color="black",alpha=0.5)+
  geom_text_repel(size=2,fontface="bold",force=20,data=
subset(pathmatrix, 
abs(pathmatrix$AdjSelect) > quantile(
abs(pathmatrix$AdjSelect),1,na.rm=T) | abs(pathmatrix$p.adjust) > quantile(
abs(pathmatrix$p.adjust),0.75,na.rm=T) |
  abs(pathmatrix$FC.1) > quantile(abs(pathmatrix$FC.1),1,na.rm=T)),
label=subset(pathmatrix, 
abs(pathmatrix$AdjSelect) > quantile(abs(pathmatrix$AdjSelect),1,na.rm=T) |  
  abs(pathmatrix$p.adjust) > quantile(abs(pathmatrix$p.adjust),0.75,na.rm=T) |
  abs(pathmatrix$FC.1) > quantile(abs(pathmatrix$FC.1),1,na.rm=T))$Description,box.padding = 0.5)+xlab("IS vs WD") + ylab("Other vs Control") 
```
```{r fig.width=4}
pathmatrixsort <- pathmatrix[order(pathmatrix$FC,decreasing=T),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description)) 
ggplot(pathmatrixsort, aes(x=FC, y=Description)) +
        geom_point(aes(color = p.adjust_original))

pathmatrixsort <- pathmatrix[order(pathmatrix$FC.1,decreasing=T),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description)) 
ggplot(pathmatrixsort, aes(x=FC.1, y=Description)) +
        geom_point(aes(color = p.adjust_original))

pathmatrixsort <- pathmatrix[order(pathmatrix$FC,decreasing=F),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description)) 
pathmatrixsort <- pathmatrixsort[!pathmatrixsort$p.adjust_original > 0.01,]
library(reshape2)
pathmatrixsortISWD <- pathmatrixsort[,c(2,10,14)]
pathmatrixsortISWD$Group <- rep("ISvsWD",nrow(pathmatrixsortISWD))
pathmatrixsortCntrlISWD <- pathmatrixsort[,c(2,11,14)]
colnames(pathmatrixsortCntrlISWD)[2] <- "FC"
pathmatrixsortCntrlISWD$Group <- rep("ControlvsIS-WD",nrow(pathmatrixsortCntrlISWD))
pathmatrixsort <- rbind(pathmatrixsortISWD,pathmatrixsortCntrlISWD)
ggplot(pathmatrixsort, aes(x=FC, y=Description)) +
        geom_line(aes(group = Description)) +
        geom_point(aes(color = Group))

pathmatrixsort <- pathmatrix[order(pathmatrix$FC,decreasing=F),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description)) 
pathmatrixsort <- pathmatrixsort[!pathmatrixsort$p.adjust_original > 0.01,]
library(reshape2)
ggplot(pathmatrixsort, aes(x=FC, y=Description)) +
        geom_point(aes(color = Count))

pathmatrixsort <- pathmatrix[order(pathmatrix$FC.1,decreasing=F),]
pathmatrixsort$Description <- factor(pathmatrixsort$Description, levels = unique(pathmatrixsort$Description)) 
pathmatrixsort <- pathmatrixsort[!pathmatrixsort$p.adjust_original > 0.01,]
library(reshape2)
ggplot(pathmatrixsort, aes(x=FC.1, y=Description)) +
        geom_point(aes(color = Count))
```
```{r}
#Coexpression MOL5 and MOL6 vs MOL1

oligos.integratedOL256 <- subset(oligos.integrated,predicted.id %in% c("MOL2","MOL5","MOL6"))

Ptgdsexpression <- oligos.integratedOL256@assays$RNA@counts["Ptgds",]
Klk6expression <- oligos.integratedOL256@assays$RNA@counts["Klk6",]


ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
#plot(x=log(ExpressionCombo$Ptgds+1),y=log(ExpressionCombo$Klk6+1))
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(ExpressionCombosorted$Klk6)

ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = FALSE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(ExpressionCombosorted$Klk6)

namesMOL56 <- oligos.integratedOL256@meta.data$predicted.id 
plot(x=ExpressionCombo$Ptgds,y=oligos.integratedOL256@meta.data$prediction.score.MOL5)
plot(x=ExpressionCombo$Ptgds,y=oligos.integratedOL256@meta.data$prediction.score.MOL6)

ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
MOL6pred <- oligos.integratedOL256@meta.data$prediction.score.MOL6
names(MOL6pred) <- row.names(oligos.integratedOL256@meta.data)
barplot(MOL6pred[row.names(ExpressionCombosorted)])

MOL5pred <- oligos.integratedOL256@meta.data$prediction.score.MOL5
names(MOL5pred) <- row.names(oligos.integratedOL256@meta.data)
barplot(MOL5pred[row.names(ExpressionCombosorted)])

MOL56pred <- rowMeans(cbind(MOL5pred,MOL6pred))
barplot(MOL56pred[row.names(ExpressionCombosorted)])
plot(MOL56pred,ExpressionCombosorted$Ptgds)

ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = FALSE),]
MOL2pred <- oligos.integratedOL256@meta.data$prediction.score.MOL2
names(MOL2pred) <- row.names(oligos.integratedOL256@meta.data)
barplot(MOL2pred[row.names(ExpressionCombosorted)])

ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
pred <- (MOL56pred)-(MOL2pred)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(pred[row.names(ExpressionCombosorted)])

ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = FALSE),]
barplot(ExpressionCombosorted$Klk6)
barplot(pred[row.names(ExpressionCombosorted)])

ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
MOL56ID <-  1*oligos.integratedOL256@meta.data$predicted.id %in% c("MOL5","MOL6")
MOL2ID <-  -1*oligos.integratedOL256@meta.data$predicted.id %in% c("MOL2")
MOLID <- MOL56ID+MOL2ID
names(MOLID) <- row.names(oligos.integratedOL256@meta.data)

ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
barplot(ExpressionCombosorted$Ptgds)
barplot(MOLID[row.names(ExpressionCombosorted)])
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = TRUE),]
barplot(ExpressionCombosorted$Klk6)
barplot(MOLID[row.names(ExpressionCombosorted)])



ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6"),]))
MOLID <- droplevels(oligos.integratedOL256@meta.data$predicted.id)
names(MOLID) <- row.names(oligos.integratedOL256@meta.data)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Ptgds,decreasing = TRUE),]
ExpressionCombosorted$ID <- MOLID[row.names(ExpressionCombosorted)] 
ExpressionCombosorted$order <- seq_len(nrow(ExpressionCombosorted))
ggplot(ExpressionCombosorted, aes(x=order, fill=ID)) +
  geom_density(alpha=0.4)

ExpressionCombo <- as.data.frame(t(oligos.integratedOL256@assays$RNA@counts[c("Ptgds","Klk6","S100b","Opalin","Hopx","Apoe","Apod"),]))
MOLID <- droplevels(oligos.integratedOL256@meta.data$predicted.id)
names(MOLID) <- row.names(oligos.integratedOL256@meta.data)
ExpressionCombosorted <- ExpressionCombo[order(ExpressionCombo$Klk6,decreasing = TRUE),]
ExpressionCombosorted$ID <- MOLID[row.names(ExpressionCombosorted)] 
ExpressionCombosorted$order <- seq_len(nrow(ExpressionCombosorted))
ggplot(ExpressionCombosorted, aes(x=order, fill=ID)) +
  geom_density(alpha=0.4)

ggplot(ExpressionCombosorted, aes(x=log(Ptgds+1), fill=ID)) +
  geom_density(alpha=0.4)

ggplot(ExpressionCombosorted, aes(x=log(Klk6+1), fill=ID)) +
  geom_density(alpha=0.4)

ggplot(ExpressionCombosorted, aes(x=log(S100b+1), fill=ID)) +
  geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Opalin+1), fill=ID)) +
  geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Hopx+1), fill=ID)) +
  geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Apoe+1), fill=ID)) +
  geom_density(alpha=0.4)
ggplot(ExpressionCombosorted, aes(x=log(Apod+1), fill=ID)) +
  geom_density(alpha=0.4)

ggplot(ExpressionCombosorted, aes(x=log(Klk6+1),y=log(Ptgds+1), color=ID)) +
  geom_point(alpha=0.4)

ggplot(ExpressionCombosorted, aes(x=Ptgds, fill=ID)) +
  geom_density(alpha=0.4)

ggplot(ExpressionCombosorted, aes(x=Klk6, fill=ID)) +
  geom_density(alpha=0.4)


```